





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
      from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image





class UpperCAmelCase_				:


  def __init__(							self ,__snake_case ,__snake_case=3 ,__snake_case=3_2 ,__snake_case=3 ,__snake_case=1_0 ,__snake_case=[8, 1_6, 3_2, 6_4] ,__snake_case=[1, 1, 2, 1] ,__snake_case=True ,__snake_case=True ,__snake_case="relu" ,__snake_case=3 ,__snake_case=None ,__snake_case=["stage2", "stage3", "stage4"] ,__snake_case=[2, 3, 4] ,__snake_case=1 ,):



       """simple docstring"""





       A_          =       parent
       A_          =       batch_size
       A_          =       image_size
       A_          =       num_channels
       A_          =       embeddings_size
       A_          =       hidden_sizes
       A_          =       depths
       A_          =       is_training
       A_          =       use_labels
       A_          =       hidden_act
       A_          =       num_labels
       A_          =       scope
       A_          =       len(__snake_case )
       A_          =       out_features
       A_          =       out_indices
       A_          =       num_groups


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       A_          =       None
       if self.use_labels:
            A_          =       ids_tensor([self.batch_size] ,self.num_labels )

       A_          =       self.get_config()

       return config, pixel_values, labels


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return BitConfig(
           num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,out_features=self.out_features ,out_indices=self.out_indices ,num_groups=self.num_groups ,)


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       BitModel(config=__snake_case )
       model.to(__snake_case )
       model.eval()
       A_          =       model(__snake_case )
       self.parent.assertEqual(
           result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) ,)


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       self.num_labels
       A_          =       BitForImageClassification(__snake_case )
       model.to(__snake_case )
       model.eval()
       A_          =       model(__snake_case ,labels=__snake_case )
       self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       BitBackbone(config=__snake_case )
       model.to(__snake_case )
       model.eval()
       A_          =       model(__snake_case )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps ) ,len(config.out_features ) )
       self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.hidden_sizes[1], 4, 4] )

       # verify channels
       self.parent.assertEqual(len(model.channels ) ,len(config.out_features ) )
       self.parent.assertListEqual(model.channels ,config.hidden_sizes[1:] )

       # verify backbone works with out_features=None
       A_          =       None
       A_          =       BitBackbone(config=__snake_case )
       model.to(__snake_case )
       model.eval()
       A_          =       model(__snake_case )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps ) ,1 )
       self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.hidden_sizes[-1], 1, 1] )

       # verify channels
       self.parent.assertEqual(len(model.channels ) ,1 )
       self.parent.assertListEqual(model.channels ,[config.hidden_sizes[-1]] )




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.prepare_config_and_inputs()
       A_   ,    A_   ,    A_          =       config_and_inputs
       A_          =       {'''pixel_values''': pixel_values}
       return config, inputs_dict





@require_torch
class UpperCAmelCase_				( snake_case__     ,		snake_case__     ,		unittest.TestCase ):
  __lowerCAmelCase   :						str							=   (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  __lowerCAmelCase   :						List[Any]							=   (
      {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
      if is_torch_available()
      else {}
  )

  __lowerCAmelCase   :						Union[str, Any]							=   False
  __lowerCAmelCase   :						Union[str, Any]							=   False
  __lowerCAmelCase   :						str							=   False
  __lowerCAmelCase   :						List[str]							=   False
  __lowerCAmelCase   :						Union[str, Any]							=   False


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       BitModelTester(self )
       A_          =       ConfigTester(self ,config_class=__snake_case ,has_text_modality=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return


  @unittest.skip(reason='''Bit does not output attentions''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass


  @unittest.skip(reason='''Bit does not use inputs_embeds''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass


  @unittest.skip(reason='''Bit does not support input and output embeddings''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_   ,    A_          =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            A_          =       model_class(__snake_case )
            A_          =       inspect.signature(model.forward )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A_          =       [*signature.parameters.keys()]

            A_          =       ['''pixel_values''']
            self.assertListEqual(arg_names[:1] ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_   ,    A_          =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            A_          =       model_class(config=__snake_case )
            for name, module in model.named_modules():
                 if isinstance(__snake_case ,(nn.BatchNormad, nn.GroupNorm) ):
                      self.assertTrue(
                          torch.all(module.weight == 1 ) ,msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,)
                      self.assertTrue(
                          torch.all(module.bias == 0 ) ,msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,)


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       def check_hidden_states_output(__snake_case ,__snake_case ,__snake_case ):
            A_          =       model_class(__snake_case )
            model.to(__snake_case )
            model.eval()

            with torch.no_grad():
                 A_          =       model(**self._prepare_for_class(__snake_case ,__snake_case ) )

            A_          =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            A_          =       self.model_tester.num_stages
            self.assertEqual(len(__snake_case ) ,expected_num_stages + 1 )

            # Bit's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)

       A_   ,    A_          =       self.model_tester.prepare_config_and_inputs_for_common()
       A_          =       ['''preactivation''', '''bottleneck''']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 A_          =       layer_type
                 A_          =       True
                 check_hidden_states_output(__snake_case ,__snake_case ,__snake_case )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 A_          =       True

                 check_hidden_states_output(__snake_case ,__snake_case ,__snake_case )


  @unittest.skip(reason='''Bit does not use feedforward chunking''' )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       pass


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__snake_case )




  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A_          =       BitModel.from_pretrained(__snake_case )
            self.assertIsNotNone(__snake_case )




def      UpperCAmelCase_    (    )	->				List[str]:
     '''simple docstring'''


     A_          =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image





@require_torch
@require_vision
class UpperCAmelCase_				( unittest.TestCase ):


  @cached_property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
       )




  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__snake_case )

       A_          =       self.default_image_processor
       A_          =       prepare_img()
       A_          =       image_processor(images=__snake_case ,return_tensors='''pt''' ).to(__snake_case )

       # forward pass
       with torch.no_grad():
            A_          =       model(**__snake_case )

       # verify the logits
       A_          =       torch.Size((1, 1_0_0_0) )
       self.assertEqual(outputs.logits.shape ,__snake_case )

       A_          =       torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(__snake_case )

       self.assertTrue(torch.allclose(outputs.logits[0, :3] ,__snake_case ,atol=1E-4 ) )





@require_torch
class UpperCAmelCase_				( snake_case__     ,		unittest.TestCase ):
  __lowerCAmelCase   :						Union[str, Any]							=   (BitBackbone,) if is_torch_available() else ()
  __lowerCAmelCase   :						List[str]							=   BitConfig

  __lowerCAmelCase   :						int							=   False


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       BitModelTester(self )







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							StableDiffusionPipeline.from_pretrained(UpperCamelCase_				,torch_dtype=torch.floataa							)

     # load LoRA weight from .safetensors
     snake_case											=							load_file(UpperCamelCase_							)

     snake_case											=							[]

     # directly update weight in diffusers model
     for key in state_dict:
          # it is suggested to print out the key, it usually will be something like below
          # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

          # as we have set the alpha beforehand, so just skip
          if ".alpha" in key or key in visited:
               continue

          if "text" in key:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.text_encoder
          else:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_UNET + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.unet

          # find the target layer
          snake_case											=							layer_infos.pop(0							)
          while len(UpperCamelCase_							) > -1:
               try:
                    snake_case											=							curr_layer.__getattr__(UpperCamelCase_							)
                    if len(UpperCamelCase_							) > 0:
                         snake_case											=							layer_infos.pop(0							)
                    elif len(UpperCamelCase_							) == 0:
                         break
               except Exception:
                    if len(UpperCamelCase_							) > 0:
                         temp_name += "_" + layer_infos.pop(0							)
                    else:
                         snake_case											=							layer_infos.pop(0							)

          snake_case											=							[]
          if "lora_down" in key:
               pair_keys.append(key.replace('''lora_down'''				,'''lora_up'''							)							)
               pair_keys.append(UpperCamelCase_							)
          else:
               pair_keys.append(UpperCamelCase_							)
               pair_keys.append(key.replace('''lora_up'''				,'''lora_down'''							)							)

          # update weight
          if len(state_dict[pair_keys[0]].shape							) == 4:
               snake_case											=							state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							).unsqueeze(2							).unsqueeze(3							)
          else:
               snake_case											=							state_dict[pair_keys[0]].to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							)

          # update visited list
          for item in pair_keys:
               visited.append(UpperCamelCase_							)

     return pipeline


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Dict      =       argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       parser.parse_args()

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       args.base_model_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.checkpoint_path
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.dump_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.lora_prefix_unet
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.lora_prefix_text_encoder
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       args.alpha

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 _SCREAMING_SNAKE_CASE     :					Tuple      =       pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




